Advances in AI-Driven Biomass Processing: A Review of Conversion Technologies, Optimization Strategies, and Smart Energy Integration

Abstract

The present paper explores the revolutionary potential of artificial intelligence (AI) in developing fuel efficiency and biomass conversion technologies to power the sustainable energy system. In light of growing concerns about environmental degradation and dependency on fossil fuels, AI models such as support vector machines and neural networks optimize fuel performance and modify energy dynamics in various disciplines, from fuel cells to biomass systems. This article summarizes the application of AI in optimizing parameters for different methods of biomass conversion (e.g., anaerobic digestion, gasification, pyrolysis, enzymatic hydrolysis, and briquetting) to enhance energy production while minimizing emissions simultaneously. The key findings of this review paper indicate that AI tools for energy trading markets enable quick logistics and decentralized energy generation to encourage the local economic and ecological benefits of such electricity exchange. Despite its advantages, bringing AI into actuary practice or business has many challenges, such as data privacy issues, algorithm bias, and compliance issues. Moreover, this paper intends to explore these issues and highlight the future prospects of AI-augmented energy systems as agents for addressing global sustainability challenges. These results indicate that a holistic AI framework may lead to robust and effective energy infrastructures with reduced resource inefficiency.